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Error Bounds for lp‐Norm Multiple Kernel Learning with Least Square Loss

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  • Shao-Gao Lv
  • Jin-De Zhu

Abstract

The problem of learning the kernel function with linear combinations of multiple kernels has attracted considerable attention recently in machine learning. Specially, by imposing an lp‐norm penalty on the kernel combination coefficient, multiple kernel learning (MKL) was proved useful and effective for theoretical analysis and practical applications (Kloft et al., 2009, 2011). In this paper, we present a theoretical analysis on the approximation error and learning ability of the lp‐norm MKL. Our analysis shows explicit learning rates for lp‐norm MKL and demonstrates some notable advantages compared with traditional kernel‐based learning algorithms where the kernel is fixed.

Suggested Citation

  • Shao-Gao Lv & Jin-De Zhu, 2012. "Error Bounds for lp‐Norm Multiple Kernel Learning with Least Square Loss," Abstract and Applied Analysis, John Wiley & Sons, vol. 2012(1).
    • Handle: RePEc:wly:jnlaaa:v:2012:y:2012:i:1:n:915920
    DOI: 10.1155/2012/915920
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    References listed on IDEAS

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    1. Ming Yuan & Yi Lin, 2006. "Model selection and estimation in regression with grouped variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(1), pages 49-67, February.
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    Cited by:

    1. Baohuai Sheng & Peixin Ye, 2013. "The Learning Rates of Regularized Regression Based on Reproducing Kernel Banach Spaces," Abstract and Applied Analysis, John Wiley & Sons, vol. 2013(1).

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